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Related Concept Videos

Fault Types01:18

Fault Types

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When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
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Discrete Fourier Transform01:15

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Expected Frequencies in Goodness-of-Fit Tests01:19

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Dynamic Modulus of Elasticity of Concrete01:16

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The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
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Power System Three-Phase Short Circuits01:21

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Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
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Routh-Hurwitz Criterion I

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Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
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Related Experiment Video

Updated: Aug 9, 2025

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
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Research on Structurally Constrained KELM Fault-Diagnosis Model Based on Frequency-Domain Fuzzy Entropy.

Xiaosu Feng1, Guanghui Zhang1, Xuyi Yuan1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary

This study introduces a novel method for diagnosing check valve faults using frequency-domain fuzzy entropy and a structurally constrained kernel extreme-learning machine model. This approach accurately identifies check valve operational states and improves fault diagnosis accuracy to 96.67%.

Keywords:
fault diagnosisfrequency-domain fuzzy entropysmoothing prior approachstructural-constrained kernel extreme-learning machine

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Area of Science:

  • Mechanical Engineering
  • Vibration Analysis
  • Fault Diagnosis

Background:

  • Check valves in high-pressure diaphragm pumps operate under complex conditions.
  • Vibration signals from check valves exhibit non-stationary and nonlinear characteristics, complicating analysis.

Purpose of the Study:

  • To accurately describe the non-linear dynamics of check valves.
  • To develop an effective fault diagnosis model for check valves.

Main Methods:

  • Decomposition of vibration signals using Smoothing Prior Analysis (SPA) into tendency and fluctuation terms.
  • Calculation of Frequency-Domain Fuzzy Entropy (FFE) for signal components.
  • Development of a Structurally Constrained Kernel Extreme-Learning Machine (SC-KELM) fault diagnosis model using function norm regularization.

Main Results:

  • Frequency-Domain Fuzzy Entropy (FFE) effectively characterizes check valve operational states.
  • The SC-KELM model demonstrates improved generalization capabilities.
  • The proposed fault diagnosis model achieved a high accuracy rate of 96.67%.

Conclusions:

  • FFE is a reliable indicator for assessing check valve operational status.
  • The SC-KELM model offers enhanced accuracy and generalization for check valve fault diagnosis.
  • This methodology provides a robust solution for ensuring the reliability of high-pressure diaphragm pump systems.